Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations822
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.1 KiB
Average record size in memory244.2 B

Variable types

Categorical6
Numeric10

Dataset

DescriptionEste es un analisis preeliminar para comprender de mejor forma los datos de nuestro dataset
AuthorKenneth David Leonel Triana , Juan Jose Naranjo, Alejandro Mora
URLhttps://github.com/kennethLeonel/Monografia-calidad-del-aire-valle-de-aburra

Alerts

anio has constant value "2024"Constant
festivo is highly imbalanced (72.5%)Imbalance
p1 is highly imbalanced (52.0%)Imbalance
codigoserial is uniformly distributedUniform
dia_semana is uniformly distributedUniform
estacion is uniformly distributedUniform
presion has 242 (29.4%) zerosZeros

Reproduction

Analysis started2024-10-16 20:45:43.530768
Analysis finished2024-10-16 20:46:08.475123
Duration24.94 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

anio
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.4 KiB
2024
822 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3288
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 822
100.0%

Length

2024-10-16T15:46:08.636379image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T15:46:08.808546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2024 822
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1644
50.0%
0 822
25.0%
4 822
25.0%

mes
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0072993
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2024-10-16T15:46:08.962727image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5816641
Coefficient of variation (CV)0.51558014
Kurtosis-1.2270774
Mean5.0072993
Median Absolute Deviation (MAD)2
Skewness-0.010631573
Sum4116
Variance6.6649893
MonotonicityNot monotonic
2024-10-16T15:46:09.210857image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 93
11.3%
3 93
11.3%
5 93
11.3%
7 93
11.3%
8 93
11.3%
4 90
10.9%
6 90
10.9%
9 90
10.9%
2 87
10.6%
ValueCountFrequency (%)
1 93
11.3%
2 87
10.6%
3 93
11.3%
4 90
10.9%
5 93
11.3%
6 90
10.9%
7 93
11.3%
8 93
11.3%
9 90
10.9%
ValueCountFrequency (%)
9 90
10.9%
8 93
11.3%
7 93
11.3%
6 90
10.9%
5 93
11.3%
4 90
10.9%
3 93
11.3%
2 87
10.6%
1 93
11.3%

dia
Real number (ℝ)

Distinct31
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729927
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.6 KiB
2024-10-16T15:46:09.404909image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8023391
Coefficient of variation (CV)0.55959186
Kurtosis-1.1958321
Mean15.729927
Median Absolute Deviation (MAD)8
Skewness0.0050555266
Sum12930
Variance77.481174
MonotonicityNot monotonic
2024-10-16T15:46:09.658019image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 27
 
3.3%
2 27
 
3.3%
29 27
 
3.3%
28 27
 
3.3%
27 27
 
3.3%
26 27
 
3.3%
25 27
 
3.3%
24 27
 
3.3%
23 27
 
3.3%
22 27
 
3.3%
Other values (21) 552
67.2%
ValueCountFrequency (%)
1 27
3.3%
2 27
3.3%
3 27
3.3%
4 27
3.3%
5 27
3.3%
6 27
3.3%
7 27
3.3%
8 27
3.3%
9 27
3.3%
10 27
3.3%
ValueCountFrequency (%)
31 15
1.8%
30 24
2.9%
29 27
3.3%
28 27
3.3%
27 27
3.3%
26 27
3.3%
25 27
3.3%
24 27
3.3%
23 27
3.3%
22 27
3.3%

pm25
Real number (ℝ)

Distinct608
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.97324
Minimum-9999
Maximum99999
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.5%
Memory size12.8 KiB
2024-10-16T15:46:09.881316image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile10.53648
Q114.391738
median18.408425
Q324.935037
95-th percentile37.611642
Maximum99999
Range109998
Interquartile range (IQR)10.5433

Descriptive statistics

Standard deviation4980.0982
Coefficient of variation (CV)23.166131
Kurtosis392.58198
Mean214.97324
Median Absolute Deviation (MAD)4.747375
Skewness19.601092
Sum176708.01
Variance24801378
MonotonicityNot monotonic
2024-10-16T15:46:10.110719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 14
 
1.7%
19 11
 
1.3%
16.5 11
 
1.3%
18.5 10
 
1.2%
15 9
 
1.1%
18 9
 
1.1%
13.5 8
 
1.0%
20 8
 
1.0%
17.5 7
 
0.9%
14 7
 
0.9%
Other values (598) 728
88.6%
ValueCountFrequency (%)
-9999 4
0.5%
1 3
0.4%
5.36807 1
 
0.1%
5.56998 1
 
0.1%
6.12773 1
 
0.1%
6.662345 1
 
0.1%
6.685655 1
 
0.1%
7.309475 1
 
0.1%
7.5 2
0.2%
7.85481 1
 
0.1%
ValueCountFrequency (%)
99999 2
0.2%
49 1
0.1%
46.81495 1
0.1%
46.5 1
0.1%
46.2742 1
0.1%
45.8667 1
0.1%
45.5 1
0.1%
45 1
0.1%
44.8498 1
0.1%
44.8023 1
0.1%

codigoserial
Categorical

UNIFORM 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size53.8 KiB
28
274 
69
274 
86
274 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1644
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28
2nd row28
3rd row28
4th row28
5th row28

Common Values

ValueCountFrequency (%)
28 274
33.3%
69 274
33.3%
86 274
33.3%

Length

2024-10-16T15:46:10.332993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T15:46:10.537004image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
28 274
33.3%
69 274
33.3%
86 274
33.3%

Most occurring characters

ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 548
33.3%
6 548
33.3%
2 274
16.7%
9 274
16.7%

dia_semana
Categorical

UNIFORM 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
Lunes
120 
Martes
117 
Miercoles
117 
Jueves
117 
Viernes
117 
Other values (2)
234 

Length

Max length9
Median length7
Mean length6.5656934
Min length5

Characters and Unicode

Total characters5397
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLunes
2nd rowMartes
3rd rowMiercoles
4th rowJueves
5th rowViernes

Common Values

ValueCountFrequency (%)
Lunes 120
14.6%
Martes 117
14.2%
Miercoles 117
14.2%
Jueves 117
14.2%
Viernes 117
14.2%
Sabado 117
14.2%
Domingo 117
14.2%

Length

2024-10-16T15:46:10.769087image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T15:46:10.989421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
lunes 120
14.6%
martes 117
14.2%
miercoles 117
14.2%
jueves 117
14.2%
viernes 117
14.2%
sabado 117
14.2%
domingo 117
14.2%

Most occurring characters

ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 939
17.4%
s 588
10.9%
o 468
 
8.7%
n 354
 
6.6%
i 351
 
6.5%
a 351
 
6.5%
r 351
 
6.5%
u 237
 
4.4%
M 234
 
4.3%
L 120
 
2.2%
Other values (12) 1404
26.0%

estacion
Categorical

UNIFORM 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
Estacion Itagui
274 
Estacion Caldas
274 
Estacion Aranjuez
274 

Length

Max length17
Median length15
Mean length15.666667
Min length15

Characters and Unicode

Total characters12878
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstacion Itagui
2nd rowEstacion Itagui
3rd rowEstacion Itagui
4th rowEstacion Itagui
5th rowEstacion Itagui

Common Values

ValueCountFrequency (%)
Estacion Itagui 274
33.3%
Estacion Caldas 274
33.3%
Estacion Aranjuez 274
33.3%

Length

2024-10-16T15:46:11.249107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T15:46:11.438593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
estacion 822
50.0%
itagui 274
 
16.7%
caldas 274
 
16.7%
aranjuez 274
 
16.7%

Most occurring characters

ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1918
14.9%
t 1096
 
8.5%
i 1096
 
8.5%
n 1096
 
8.5%
s 1096
 
8.5%
E 822
 
6.4%
c 822
 
6.4%
o 822
 
6.4%
822
 
6.4%
u 548
 
4.3%
Other values (10) 2740
21.3%

festivo
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size53.0 KiB
0
783 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters822
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Length

2024-10-16T15:46:11.621795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T15:46:11.775135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 783
95.3%
1 39
 
4.7%

temperatura
Real number (ℝ)

Distinct219
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-74.148966
Minimum-999
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:11.977460image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q119.5
median21.3
Q322.9
95-th percentile24.195
Maximum25.5
Range1024.5
Interquartile range (IQR)3.4

Descriptive statistics

Standard deviation297.51698
Coefficient of variation (CV)-4.0124225
Kurtosis5.8206594
Mean-74.148966
Median Absolute Deviation (MAD)1.6975
Skewness-2.7939302
Sum-60950.45
Variance88516.354
MonotonicityNot monotonic
2024-10-16T15:46:12.228410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
21 20
 
2.4%
23.5 16
 
1.9%
22.4 16
 
1.9%
23.4 15
 
1.8%
23 15
 
1.8%
20.1 14
 
1.7%
22.5 14
 
1.7%
22.9 14
 
1.7%
21.9 14
 
1.7%
Other values (209) 607
73.8%
ValueCountFrequency (%)
-999 77
9.4%
16.1 1
 
0.1%
16.5 1
 
0.1%
16.7 1
 
0.1%
16.8 3
 
0.4%
16.9 2
 
0.2%
17 5
 
0.6%
17.1 1
 
0.1%
17.2 4
 
0.5%
17.3 4
 
0.5%
ValueCountFrequency (%)
25.5 1
 
0.1%
25.375 1
 
0.1%
25.1 1
 
0.1%
25.09 1
 
0.1%
25 1
 
0.1%
24.995 1
 
0.1%
24.9 1
 
0.1%
24.85 1
 
0.1%
24.8 4
0.5%
24.799999 1
 
0.1%

humedad
Real number (ℝ)

Distinct426
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-25.815931
Minimum-999
Maximum91.8
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:12.450631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q165.8125
median74.05
Q381.15
95-th percentile86.1
Maximum91.8
Range1090.8
Interquartile range (IQR)15.3375

Descriptive statistics

Standard deviation313.16084
Coefficient of variation (CV)-12.130527
Kurtosis5.8102623
Mean-25.815931
Median Absolute Deviation (MAD)7.55
Skewness-2.7907922
Sum-21220.695
Variance98069.709
MonotonicityNot monotonic
2024-10-16T15:46:12.685340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
81 14
 
1.7%
78 12
 
1.5%
73 12
 
1.5%
82 12
 
1.5%
76 12
 
1.5%
85 11
 
1.3%
75 10
 
1.2%
84 10
 
1.2%
80 10
 
1.2%
Other values (416) 642
78.1%
ValueCountFrequency (%)
-999 77
9.4%
50.2 1
 
0.1%
51.25 1
 
0.1%
52.15 1
 
0.1%
52.15 1
 
0.1%
52.5 2
 
0.2%
52.9 1
 
0.1%
54 1
 
0.1%
54.2 1
 
0.1%
54.95 1
 
0.1%
ValueCountFrequency (%)
91.8 1
 
0.1%
91.43 1
 
0.1%
91 1
 
0.1%
90.9 1
 
0.1%
89.5 1
 
0.1%
89 3
0.4%
88.8 1
 
0.1%
88.68 1
 
0.1%
88.55 1
 
0.1%
88.5 1
 
0.1%

presion
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.48345
Minimum-999
Maximum854.6
Zeros242
Zeros (%)29.4%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:12.909587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q10
median826
Q3851.5
95-th percentile853
Maximum854.6
Range1853.6
Interquartile range (IQR)851.5

Descriptive statistics

Standard deviation590.81487
Coefficient of variation (CV)1.4050847
Kurtosis0.37110377
Mean420.48345
Median Absolute Deviation (MAD)26.5
Skewness-1.1897845
Sum345637.4
Variance349062.21
MonotonicityNot monotonic
2024-10-16T15:46:13.145144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 242
29.4%
-999 77
 
9.4%
825.8 17
 
2.1%
826.7 17
 
2.1%
852.4 16
 
1.9%
852.3 15
 
1.8%
852.1 15
 
1.8%
826.5 14
 
1.7%
852.9 13
 
1.6%
852 13
 
1.6%
Other values (85) 383
46.6%
ValueCountFrequency (%)
-999 77
 
9.4%
0 242
29.4%
823.5 1
 
0.1%
824.1 1
 
0.1%
824.2 1
 
0.1%
824.3 2
 
0.2%
824.4 1
 
0.1%
824.6 3
 
0.4%
824.7 4
 
0.5%
824.8 5
 
0.6%
ValueCountFrequency (%)
854.6 1
 
0.1%
854.1 2
0.2%
854 1
 
0.1%
853.9 4
0.5%
853.85 1
 
0.1%
853.8 2
0.2%
853.7 2
0.2%
853.6 2
0.2%
853.55 1
 
0.1%
853.5 2
0.2%

p1
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
0.0
737 
-999.0
85 

Length

Max length6
Median length3
Mean length3.310219
Min length3

Characters and Unicode

Total characters2721
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 737
89.7%
-999.0 85
 
10.3%

Length

2024-10-16T15:46:13.392421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-16T15:46:13.589571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.0 737
89.7%
999.0 85
 
10.3%

Most occurring characters

ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1559
57.3%
. 822
30.2%
9 255
 
9.4%
- 85
 
3.1%

velocidad_prom
Real number (ℝ)

Distinct163
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.201259
Minimum-999
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:13.813914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.11125
median1.415
Q31.7875
95-th percentile2.28
Maximum3.5
Range1002.5
Interquartile range (IQR)0.67625

Descriptive statistics

Standard deviation291.70438
Coefficient of variation (CV)-3.1637787
Kurtosis5.8212824
Mean-92.201259
Median Absolute Deviation (MAD)0.315
Skewness-2.7941186
Sum-75789.435
Variance85091.443
MonotonicityNot monotonic
2024-10-16T15:46:14.079914image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
1.4 58
 
7.1%
1.5 58
 
7.1%
1.2 58
 
7.1%
1.6 47
 
5.7%
1.3 47
 
5.7%
1.8 33
 
4.0%
1.7 31
 
3.8%
1.1 25
 
3.0%
1 22
 
2.7%
Other values (153) 366
44.5%
ValueCountFrequency (%)
-999 77
9.4%
0.1 2
 
0.2%
0.2 1
 
0.1%
0.5 1
 
0.1%
0.6 9
 
1.1%
0.7 19
 
2.3%
0.8 18
 
2.2%
0.9 15
 
1.8%
0.93 1
 
0.1%
0.99 2
 
0.2%
ValueCountFrequency (%)
3.5 1
0.1%
3.3 1
0.1%
3.1 2
0.2%
3 2
0.2%
2.9 1
0.1%
2.7 2
0.2%
2.61 1
0.1%
2.6 2
0.2%
2.59 1
0.1%
2.5 1
0.1%

velocidad_max
Real number (ℝ)

Distinct57
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.358942
Minimum-999
Maximum5
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:14.480244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q11.8
median2.3
Q32.9
95-th percentile3.5
Maximum5
Range1004
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation291.97579
Coefficient of variation (CV)-3.1959191
Kurtosis5.8212299
Mean-91.358942
Median Absolute Deviation (MAD)0.6
Skewness-2.7941027
Sum-75097.05
Variance85249.86
MonotonicityNot monotonic
2024-10-16T15:46:15.284368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
1.9 47
 
5.7%
2.9 39
 
4.7%
2.2 37
 
4.5%
2 37
 
4.5%
1.8 37
 
4.5%
2.3 37
 
4.5%
1.7 36
 
4.4%
3.1 35
 
4.3%
2.8 35
 
4.3%
Other values (47) 405
49.3%
ValueCountFrequency (%)
-999 77
9.4%
0.3 1
 
0.1%
0.4 1
 
0.1%
0.55 1
 
0.1%
0.9 1
 
0.1%
1 3
 
0.4%
1.1 7
 
0.9%
1.2 8
 
1.0%
1.3 11
 
1.3%
1.4 13
 
1.6%
ValueCountFrequency (%)
5 1
 
0.1%
4.7 1
 
0.1%
4.5 2
 
0.2%
4.4 5
0.6%
4.2 1
 
0.1%
4.1 2
 
0.2%
4 4
0.5%
3.9 3
0.4%
3.85 1
 
0.1%
3.8 3
0.4%

direccion_prom
Real number (ℝ)

Distinct336
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.837591
Minimum-999
Maximum338
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:15.562647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q174.125
median130
Q3167
95-th percentile272
Maximum338
Range1337
Interquartile range (IQR)92.875

Descriptive statistics

Standard deviation338.53405
Coefficient of variation (CV)9.7174929
Kurtosis5.246014
Mean34.837591
Median Absolute Deviation (MAD)46
Skewness-2.6116487
Sum28636.5
Variance114605.3
MonotonicityNot monotonic
2024-10-16T15:46:15.868866image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
135 14
 
1.7%
124 13
 
1.6%
138 12
 
1.5%
134 12
 
1.5%
126 12
 
1.5%
129 12
 
1.5%
131 10
 
1.2%
125 10
 
1.2%
130 9
 
1.1%
Other values (326) 641
78.0%
ValueCountFrequency (%)
-999 77
9.4%
0.5 1
 
0.1%
4.5 1
 
0.1%
29 1
 
0.1%
30 2
 
0.2%
32 1
 
0.1%
32.5 1
 
0.1%
33 2
 
0.2%
34 2
 
0.2%
35 1
 
0.1%
ValueCountFrequency (%)
338 1
 
0.1%
335.5 1
 
0.1%
327 3
0.4%
326 3
0.4%
322.5 1
 
0.1%
321.5 1
 
0.1%
320 1
 
0.1%
318 1
 
0.1%
315 1
 
0.1%
313.5 1
 
0.1%

direccion_max
Real number (ℝ)

Distinct302
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.058394
Minimum-999
Maximum333
Zeros0
Zeros (%)0.0%
Negative77
Negative (%)9.4%
Memory size12.8 KiB
2024-10-16T15:46:16.154730image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q171.25
median141
Q3187
95-th percentile264
Maximum333
Range1332
Interquartile range (IQR)115.75

Descriptive statistics

Standard deviation340.14222
Coefficient of variation (CV)8.4911596
Kurtosis5.2506013
Mean40.058394
Median Absolute Deviation (MAD)58
Skewness-2.6174331
Sum32928
Variance115696.73
MonotonicityNot monotonic
2024-10-16T15:46:16.460532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-999 77
 
9.4%
182 14
 
1.7%
199 12
 
1.5%
150 12
 
1.5%
141 12
 
1.5%
175 11
 
1.3%
180 10
 
1.2%
68 9
 
1.1%
174 9
 
1.1%
45 8
 
1.0%
Other values (292) 648
78.8%
ValueCountFrequency (%)
-999 77
9.4%
0.5 1
 
0.1%
4 1
 
0.1%
35 1
 
0.1%
39 2
 
0.2%
40 3
 
0.4%
42 4
 
0.5%
43 2
 
0.2%
44 3
 
0.4%
45 8
 
1.0%
ValueCountFrequency (%)
333 1
0.1%
325.5 1
0.1%
319 1
0.1%
313 2
0.2%
302 1
0.1%
299 2
0.2%
298.5 2
0.2%
297.5 1
0.1%
292 2
0.2%
289 1
0.1%

Interactions

2024-10-16T15:46:05.770732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:44.459628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:48.118918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:51.060291image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:54.152772image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:56.027480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:57.870736image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:00.046939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:01.816295image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:03.716510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:05.965702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:44.893085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:48.615167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:51.671480image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:54.361528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:56.220987image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:58.105418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:00.253439image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:02.002332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:03.910785image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:06.168227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:45.265632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:48.983743image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:51.975020image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:54.532685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:56.384911image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:58.308654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:00.435324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:02.194134image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:04.118092image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:06.350729image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:45.710896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:49.365563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:52.310815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:54.729619image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:56.569317image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:58.489314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:00.599389image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:02.369614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:04.384517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:06.794678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:45.954793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:49.646562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:52.889467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:54.931016image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:56.746851image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:58.665473image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:00.762892image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:02.599486image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:04.577995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:06.967077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:46.315083image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:49.864342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:53.106509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:55.141808image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:56.939675image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:59.085608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:00.921463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:02.791900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:04.766726image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:07.150457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:46.684831image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:50.120680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:53.399269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:55.348228image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:57.154949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:59.259848image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:01.099504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:03.015172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:04.979048image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:07.319133image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:46.928517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:50.330947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:53.596124image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:55.521898image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:57.326648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:59.464667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:01.279752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:03.202869image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:05.193305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:07.489027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:47.147331image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:50.494116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:53.790840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:55.697664image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:57.498854image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:59.636076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:01.458507image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:03.368622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:05.402972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:07.668265image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:47.493457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:50.702616image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:53.977508image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:55.864169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:57.686431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:45:59.849876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:01.634827image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:03.549169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-10-16T15:46:05.600454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Missing values

2024-10-16T15:46:07.947423image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-16T15:46:08.305160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

aniomesdiapm25codigoserialdia_semanaestacionfestivotemperaturahumedadpresionp1velocidad_promvelocidad_maxdireccion_promdireccion_max
020241118.528LunesEstacion Itagui121.98000081.0000000.00.01.7602.50107.584.0
120241211.028MartesEstacion Itagui021.58000083.0000000.00.02.0802.9055.056.0
220241313.028MiercolesEstacion Itagui021.40000076.2100000.00.01.8702.70114.093.0
320241421.028JuevesEstacion Itagui021.80499979.0000000.00.01.7602.55164.0151.0
420241519.028ViernesEstacion Itagui020.94500079.3200000.00.01.9402.80167.5175.0
520241616.028SabadoEstacion Itagui022.10000074.0900000.00.01.9052.80170.0182.5
620241711.028DomingoEstacion Itagui022.21000074.8849980.00.02.4903.5062.060.0
720241818.028LunesEstacion Itagui122.50000077.2300000.00.01.8602.60166.0182.0
820241921.028MartesEstacion Itagui022.90000080.0000000.00.01.7902.60164.0184.0
9202411023.028MiercolesEstacion Itagui022.50000081.0000000.00.02.0252.9034.043.0
aniomesdiapm25codigoserialdia_semanaestacionfestivotemperaturahumedadpresionp1velocidad_promvelocidad_maxdireccion_promdireccion_max
264202492119.4462086SabadoEstacion Aranjuez023.3062.10851.00.01.11.9212.0210.0
265202492214.2974086DomingoEstacion Aranjuez020.6080.90851.60.01.01.7228.0226.0
266202492314.0104086LunesEstacion Aranjuez021.3078.35852.40.01.22.346.073.0
267202492415.5602586MartesEstacion Aranjuez023.4068.40851.80.01.42.849.062.0
268202492516.6063586MiercolesEstacion Aranjuez022.8070.95851.90.01.22.2199.0194.0
269202492611.4411586JuevesEstacion Aranjuez019.9080.10852.90.00.91.7247.0238.0
270202492727.4915086ViernesEstacion Aranjuez019.8579.20852.40.00.61.1273.0270.0
271202492826.7997586SabadoEstacion Aranjuez019.6081.70852.50.00.81.5198.0194.0
272202492915.1852086DomingoEstacion Aranjuez018.7087.30853.30.00.61.1266.5256.5
273202493019.7981586LunesEstacion Aranjuez019.8081.70852.90.00.71.3258.5246.0